Importing importatn lubraries

from nsepy import get_history
from datetime import date
import pandas as pd
import numpy as np
import seaborn as sns
sns=sns.set()
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
#import pandas_datareader as pdr/

Getting data from NSE official website

Enter the stock name(listed symbol)

f you want to get data of NIFTY or BANKNIFTY then answer 'YES' if you want to get data of stocks then asnwer 'NO'

after this enter the stock name(listed symbol) or index name(NIFTY/BANKNIFTY), remember the stock symbols are case sensitie so kindly enter proper symbol

stock_type=input('want to check index?: ' )
if stock_type=='YES':
    df= get_history(symbol=input('Enter stock name: ' ),
                            start=date(2017,1,1),
                            end=date.today(),
                            index=True)
elif stock_type=='NO':
    df= get_history(symbol=input('Enter stock name: '),
                            start=date(2020,1,1),
                            end=date.today())
else: 
    print('Please enter valid stock symbol or INDEX ')

want to check index?: YES
Enter stock name: BANKNIFTY

df.head()

	Open	High	Low	Close	Volume	Turnover
Date
2017-01-02	18242.30	18249.00	17844.90	17969.60	74444730.0	1.857240e+10
2017-01-03	18002.75	18115.05	17830.95	18035.60	64513818.0	1.769300e+10
2017-01-04	18037.45	18092.85	17868.90	17891.00	50508161.0	1.615850e+10
2017-01-05	18000.75	18164.05	17977.80	18115.95	68874940.0	2.271180e+10
2017-01-06	18168.45	18325.50	18157.30	18264.00	65919927.0	2.174590e+10

df=df[['Open','High','Low','Close','Volume','Turnover']]

df.head()

	Open	High	Low	Close	Volume	Turnover
Date
2020-01-01	334.70	335.95	332.15	334.45	17379320	5.805130e+14
2020-01-02	334.50	339.85	333.35	339.30	20324236	6.845341e+14
2020-01-03	337.95	337.95	332.00	333.70	21853208	7.322600e+14
2020-01-06	331.70	331.70	317.70	319.00	35645325	1.147882e+15
2020-01-07	324.45	327.00	315.40	318.40	50966826	1.632868e+15

#shape of data
df.shape

(1423, 6)

df.info()

<class 'pandas.core.frame.DataFrame'>
Index: 1423 entries, 2017-01-02 to 2022-09-28
Data columns (total 6 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   Open      1423 non-null   float64
 1   High      1423 non-null   float64
 2   Low       1423 non-null   float64
 3   Close     1423 non-null   float64
 4   Volume    1412 non-null   float64
 5   Turnover  1412 non-null   float64
dtypes: float64(6)
memory usage: 77.8+ KB

Data Cleaning

df.isnull().sum()

Open         0
High         0
Low          0
Close        0
Volume      11
Turnover    11
dtype: int64

df['Turnover']

Date
2017-01-02    1.857240e+10
2017-01-03    1.769300e+10
2017-01-04    1.615850e+10
2017-01-05    2.271180e+10
2017-01-06    2.174590e+10
                  ...     
2022-09-22    5.555900e+10
2022-09-23    6.487980e+10
2022-09-26    6.410060e+10
2022-09-27    5.503350e+10
2022-09-28    5.500600e+10
Name: Turnover, Length: 1423, dtype: float64

df.drop(['Turnover'], axis=1,inplace=True)

df.dropna(inplace=True)

Nifty Next 50 outperformed other broad market indexes in the last decade though lost momentum in the last two years.

df['Open'].plot(figsize=(16,6))
plt.title(' OPEN VALUE OVER PERIOD OF TIME',fontsize=15)
plt.xlabel('YEAR',fontsize=15)
plt.ylabel('NIFTY VALUE',fontsize=15)

Text(0, 0.5, 'NIFTY VALUE')

Resampling

df1=df.copy()

from datetime import datetime

	
df1.index = pd.to_datetime(df1.index)

All the minimum values of index per year

df1.resample(rule='A').min()

	Open	High	Low	Close	Volume
Date
2020-12-31	151.95	153.2	149.45	150.85	7753219
2021-12-31	271.90	280.0	269.50	275.65	8639835
2022-12-31	438.00	442.1	425.00	434.70	6945602

df1.resample(rule='QS').min()['Open'].plot(figsize=(16,6))
plt.title('INDIAN NIFTY MINIMUM VALUE PER QUARTER',fontsize=15)
plt.xlabel('YEAR',fontsize=15)
plt.ylabel('NIFTY VALUE',fontsize=15)

Text(0, 0.5, 'NIFTY VALUE')

df1_min=df1.resample(rule='QS').min()['Open']

df1_min.head()

Date
2020-01-01    178.00
2020-04-01    151.95
2020-07-01    179.00
2020-10-01    187.00
2021-01-01    271.90
Freq: QS-JAN, Name: Open, dtype: float64

df1_max=df1.resample(rule='QS').max()['Open']

df1_max.head()

Date
2020-01-01    337.95
2020-04-01    197.00
2020-07-01    231.55
2020-10-01    278.00
2021-01-01    425.05
Freq: QS-JAN, Name: Open, dtype: float64

df_min_max=pd.merge(df1_min, df1_max, left_index=True, right_index=True)

df_min_max.head()

	Open_x	Open_y
Date
2020-01-01	178.00	337.95
2020-04-01	151.95	197.00
2020-07-01	179.00	231.55
2020-10-01	187.00	278.00
2021-01-01	271.90	425.05

Nifty50 Open Maximum values plotting by bar chart

df_min_max['Open_x'].plot(kind='bar',figsize=(16,6))

<AxesSubplot:xlabel='Date'>

df1.plot(subplots = True, figsize = (12, 12));
plt.legend(loc = 'best')
plt.show()

we can see deep in market in 2020 due to covid but after few months markte recoverd so fast and even made all time high

we can see increase in volumes after covid in 2020 (Volume is the amount of an asset or security that changes hands over some period of time, often over the course of a day. For instance, stock trading volume would refer to the number of shares of a security traded between its daily open and close.)

df1.head()

	Open	High	Low	Close	Volume
Date
2020-01-01	334.70	335.95	332.15	334.45	17379320
2020-01-02	334.50	339.85	333.35	339.30	20324236
2020-01-03	337.95	337.95	332.00	333.70	21853208
2020-01-06	331.70	331.70	317.70	319.00	35645325
2020-01-07	324.45	327.00	315.40	318.40	50966826

import mplfinance as mpf

Nifty 50 behavior during each time unit is represented in the form of a candle. If the closing price of a stock is higher than open price during a particular time period, then the candle is green, if the close price is below the open price then the candle is red. Each candle has a body and two wicks. The distance between open to close is represented by the body of a candle and the upper and lower wicks represent the highs and lows of a candle.

mpf.plot(data=df1,type='candle',mav=(300),volume=True,tight_layout=True,figsize=(20,12),style='yahoo')

Stocks Emerge From Covid Crash With Historic 12-Month Run

we will implement prophet model now

importing all the important librarieas

from prophet import Prophet
import warnings
warnings.filterwarnings('ignore')  # Hide warnings
import datetime as dt
import pandas as pd
pd.core.common.is_list_like = pd.api.types.is_list_like

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.dates as mdates
from nsepy import get_history
from datetime import date

df.reset_index(inplace=True)

data=df[["Date","Close"]]

df

	Date	Open	High	Low	Close	Volume
0	2020-01-01	334.70	335.95	332.15	334.45	17379320
1	2020-01-02	334.50	339.85	333.35	339.30	20324236
2	2020-01-03	337.95	337.95	332.00	333.70	21853208
3	2020-01-06	331.70	331.70	317.70	319.00	35645325
4	2020-01-07	324.45	327.00	315.40	318.40	50966826
...	...	...	...	...	...	...
679	2022-09-22	566.75	574.00	562.00	567.30	11869435
680	2022-09-23	564.80	568.00	549.50	550.60	12563239
681	2022-09-26	545.80	549.00	533.00	543.30	16158750
682	2022-09-27	544.55	550.90	533.20	536.50	11066799
683	2022-09-28	532.90	539.80	520.35	524.85	12233485

684 rows × 6 columns

data=data.rename(columns={"Date": "ds", "Close": "y"})

len(data)

684

a=len(data)*80/100
a=int(a)
print(a)

547

df_train=data[0:a]
df_test=data[a:]

#df_train=data[0:600]
#df_test=data[600:682]

fitting model

# define the model
model = Prophet()
# fit the model
model.fit(df_train)

00:11:53 - cmdstanpy - INFO - Chain [1] start processing
00:11:54 - cmdstanpy - INFO - Chain [1] done processing

<prophet.forecaster.Prophet at 0x231f573c220>

forecast = model.predict(df_test)

forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]

	ds	yhat	yhat_lower	yhat_upper
0	2022-03-11	461.545223	441.402895	481.943996
1	2022-03-14	451.290021	432.170012	470.481047
2	2022-03-15	450.276928	430.828872	468.517713
3	2022-03-16	449.049043	429.430760	469.675613
4	2022-03-17	446.347518	426.434817	465.800359
...	...	...	...	...
132	2022-09-22	473.517644	406.161675	551.080201
133	2022-09-23	471.562586	401.366896	546.621567
134	2022-09-26	470.758254	402.348109	547.737094
135	2022-09-27	472.714958	401.293175	550.218102
136	2022-09-28	474.389872	404.252811	553.267494

137 rows × 4 columns

fig1 = model.plot(forecast)

y_pred = forecast['yhat'].values
y_true = df_test['y'].values

from sklearn.metrics import mean_squared_error, mean_absolute_error
from sklearn.metrics import mean_squared_error, r2_score
import math

print('MSE: %.3f' % mean_squared_error(y_true, y_pred))
print('R2: %.3f' % r2_score(y_true, y_pred))

MSE: 4339.327
R2: -2.847

# report performance
mse = mean_squared_error(y_true, y_pred)
print('MSE: '+str(mse))
mae = mean_absolute_error(y_true, y_pred)
print('MAE: '+str(mae))
rmse = math.sqrt(mean_squared_error(y_true, y_pred))
print('RMSE: '+str(rmse))
mape = np.mean(np.abs(y_pred - y_true)/np.abs(y_true))
print('MAPE: '+str(mape))

MSE: 4339.326770079902
MAE: 59.16928749357548
RMSE: 65.87356655047533
MAPE: 0.11616185625604351

WE WILL IMPLEMENT LSTM MODEL NOW

import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.layers import Dense, LSTM
from tensorflow.keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
pd.options.mode.chained_assignment = None
import yfinance as yf

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from nsepy import get_history
from datetime import date
from plotly.offline import init_notebook_mode, iplot, plot
import plotly as py
import plotly.express as px
from plotly.subplots import make_subplots
from sklearn.metrics import roc_curve, auc
import plotly.graph_objs as go
import plotly.figure_factory as ff
init_notebook_mode()
import warnings
warnings.filterwarnings('ignore')  # Hide warnings
import datetime as dt
import pandas as pd
pd.core.common.is_list_like = pd.api.types.is_list_like
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.dates as mdates

a=len(df)*80/100
a=int(a)
print(a)

547

train=df[0:a]
test=df[a:]

len(test)#.tail()8

137

len(train)#.tail()

547

train.tail()

	Date	Open	High	Low	Close	Volume
542	2022-03-04	460.3	470.90	457.65	461.95	20684268
543	2022-03-07	447.5	453.95	433.45	440.30	33720472
544	2022-03-08	438.0	442.10	425.00	440.30	42510207
545	2022-03-09	442.0	454.30	438.55	451.70	32331325
546	2022-03-10	469.9	476.65	461.65	468.70	34553299

test.head()

	Date	Open	High	Low	Close	Volume
547	2022-03-11	466.4	474.8	462.00	470.35	22091309
548	2022-03-14	471.0	486.0	470.55	485.15	27750689
549	2022-03-15	489.0	490.5	480.65	485.85	27204899
550	2022-03-16	493.0	494.3	489.40	492.75	16211288
551	2022-03-17	499.0	503.5	498.30	501.90	22947673

training_set = train.iloc[:, 1:2].values

len(training_set)

547

from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler(feature_range = (0, 1))
training_set_scaled = sc.fit_transform(training_set)

X_train = []
y_train = []
for i in range(60, len(training_set)):
    X_train.append(training_set_scaled[i-60:i, 0])
    y_train.append(training_set_scaled[i, 0])
X_train, y_train = np.array(X_train), np.array(y_train)

X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout

regressor = Sequential()

regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))
regressor.add(Dropout(0.2))

regressor.add(LSTM(units = 50, return_sequences = True))
regressor.add(Dropout(0.2))

regressor.add(LSTM(units = 50, return_sequences = True))
regressor.add(Dropout(0.2))

regressor.add(LSTM(units = 50))
regressor.add(Dropout(0.2))

regressor.add(Dense(units = 1))

regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')

regressor.fit(X_train, y_train, epochs = 100, batch_size = 32)

Epoch 1/100
16/16 [==============================] - 7s 70ms/step - loss: 0.0873
Epoch 2/100
16/16 [==============================] - 1s 70ms/step - loss: 0.0171
Epoch 3/100
16/16 [==============================] - 1s 73ms/step - loss: 0.0116
Epoch 4/100
16/16 [==============================] - 1s 71ms/step - loss: 0.0095
Epoch 5/100
16/16 [==============================] - 1s 65ms/step - loss: 0.0083
Epoch 6/100
16/16 [==============================] - 1s 72ms/step - loss: 0.0080
Epoch 7/100
16/16 [==============================] - 1s 66ms/step - loss: 0.0073
Epoch 8/100
16/16 [==============================] - 1s 66ms/step - loss: 0.0081
Epoch 9/100
16/16 [==============================] - 1s 66ms/step - loss: 0.0073
Epoch 10/100
16/16 [==============================] - 1s 67ms/step - loss: 0.0073
Epoch 11/100
16/16 [==============================] - 1s 68ms/step - loss: 0.0068
Epoch 12/100
16/16 [==============================] - 1s 67ms/step - loss: 0.0072
Epoch 13/100
16/16 [==============================] - 1s 78ms/step - loss: 0.0060
Epoch 14/100
16/16 [==============================] - 1s 80ms/step - loss: 0.0065
Epoch 15/100
16/16 [==============================] - 1s 80ms/step - loss: 0.0068
Epoch 16/100
16/16 [==============================] - 1s 80ms/step - loss: 0.0064
Epoch 17/100
16/16 [==============================] - 1s 79ms/step - loss: 0.0055
Epoch 18/100
16/16 [==============================] - 1s 80ms/step - loss: 0.0071
Epoch 19/100
16/16 [==============================] - 1s 79ms/step - loss: 0.0060
Epoch 20/100
16/16 [==============================] - 1s 79ms/step - loss: 0.0045
Epoch 21/100
16/16 [==============================] - 1s 80ms/step - loss: 0.0052
Epoch 22/100
16/16 [==============================] - 1s 78ms/step - loss: 0.0058
Epoch 23/100
16/16 [==============================] - 1s 79ms/step - loss: 0.0063
Epoch 24/100
16/16 [==============================] - 1s 82ms/step - loss: 0.0050
Epoch 25/100
16/16 [==============================] - 1s 78ms/step - loss: 0.0056
Epoch 26/100
16/16 [==============================] - 1s 79ms/step - loss: 0.0053
Epoch 27/100
16/16 [==============================] - 1s 80ms/step - loss: 0.0057
Epoch 28/100
16/16 [==============================] - 1s 78ms/step - loss: 0.0051
Epoch 29/100
16/16 [==============================] - 1s 79ms/step - loss: 0.0061
Epoch 30/100
16/16 [==============================] - 1s 78ms/step - loss: 0.0050
Epoch 31/100
16/16 [==============================] - 1s 78ms/step - loss: 0.0045
Epoch 32/100
16/16 [==============================] - 1s 80ms/step - loss: 0.0046
Epoch 33/100
16/16 [==============================] - 1s 77ms/step - loss: 0.0050
Epoch 34/100
16/16 [==============================] - 1s 78ms/step - loss: 0.0050
Epoch 35/100
16/16 [==============================] - 1s 78ms/step - loss: 0.0048
Epoch 36/100
16/16 [==============================] - 1s 78ms/step - loss: 0.0052
Epoch 37/100
16/16 [==============================] - 1s 80ms/step - loss: 0.0045
Epoch 38/100
16/16 [==============================] - 1s 78ms/step - loss: 0.0040
Epoch 39/100
16/16 [==============================] - 1s 77ms/step - loss: 0.0048
Epoch 40/100
16/16 [==============================] - 1s 78ms/step - loss: 0.0047
Epoch 41/100
16/16 [==============================] - 1s 77ms/step - loss: 0.0043
Epoch 42/100
16/16 [==============================] - 1s 78ms/step - loss: 0.0042
Epoch 43/100
16/16 [==============================] - 1s 78ms/step - loss: 0.0050
Epoch 44/100
16/16 [==============================] - 1s 77ms/step - loss: 0.0042
Epoch 45/100
16/16 [==============================] - 1s 84ms/step - loss: 0.0049
Epoch 46/100
16/16 [==============================] - 1s 85ms/step - loss: 0.0045
Epoch 47/100
16/16 [==============================] - 1s 84ms/step - loss: 0.0049
Epoch 48/100
16/16 [==============================] - 1s 83ms/step - loss: 0.0046
Epoch 49/100
16/16 [==============================] - 1s 78ms/step - loss: 0.0046
Epoch 50/100
16/16 [==============================] - 1s 78ms/step - loss: 0.0042
Epoch 51/100
16/16 [==============================] - 1s 81ms/step - loss: 0.0041
Epoch 52/100
16/16 [==============================] - 1s 78ms/step - loss: 0.0044
Epoch 53/100
16/16 [==============================] - 1s 77ms/step - loss: 0.0040
Epoch 54/100
16/16 [==============================] - 1s 79ms/step - loss: 0.0040
Epoch 55/100
16/16 [==============================] - 1s 76ms/step - loss: 0.0041
Epoch 56/100
16/16 [==============================] - 1s 77ms/step - loss: 0.0051
Epoch 57/100
16/16 [==============================] - 1s 79ms/step - loss: 0.0044
Epoch 58/100
16/16 [==============================] - 1s 82ms/step - loss: 0.0043
Epoch 59/100
16/16 [==============================] - 1s 77ms/step - loss: 0.0045
Epoch 60/100
16/16 [==============================] - 1s 77ms/step - loss: 0.0035
Epoch 61/100
16/16 [==============================] - 1s 78ms/step - loss: 0.0039
Epoch 62/100
16/16 [==============================] - 1s 78ms/step - loss: 0.0036
Epoch 63/100
16/16 [==============================] - 1s 76ms/step - loss: 0.0035
Epoch 64/100
16/16 [==============================] - 1s 78ms/step - loss: 0.0037
Epoch 65/100
16/16 [==============================] - 1s 78ms/step - loss: 0.0037
Epoch 66/100
16/16 [==============================] - 1s 77ms/step - loss: 0.0036
Epoch 67/100
16/16 [==============================] - 1s 77ms/step - loss: 0.0033
Epoch 68/100
16/16 [==============================] - 1s 79ms/step - loss: 0.0038
Epoch 69/100
16/16 [==============================] - 1s 77ms/step - loss: 0.0034
Epoch 70/100
16/16 [==============================] - 1s 79ms/step - loss: 0.0034
Epoch 71/100
16/16 [==============================] - 1s 77ms/step - loss: 0.0035
Epoch 72/100
16/16 [==============================] - 1s 77ms/step - loss: 0.0032
Epoch 73/100
16/16 [==============================] - 1s 77ms/step - loss: 0.0037
Epoch 74/100
16/16 [==============================] - 1s 77ms/step - loss: 0.0033
Epoch 75/100
16/16 [==============================] - 1s 78ms/step - loss: 0.0033
Epoch 76/100
16/16 [==============================] - 1s 77ms/step - loss: 0.0037
Epoch 77/100
16/16 [==============================] - 1s 77ms/step - loss: 0.0034
Epoch 78/100
16/16 [==============================] - 1s 80ms/step - loss: 0.0031
Epoch 79/100
16/16 [==============================] - 1s 85ms/step - loss: 0.0034
Epoch 80/100
16/16 [==============================] - 1s 80ms/step - loss: 0.0033
Epoch 81/100
16/16 [==============================] - 1s 88ms/step - loss: 0.0032
Epoch 82/100
16/16 [==============================] - 1s 85ms/step - loss: 0.0033
Epoch 83/100
16/16 [==============================] - 1s 88ms/step - loss: 0.0036
Epoch 84/100
16/16 [==============================] - 1s 88ms/step - loss: 0.0034
Epoch 85/100
16/16 [==============================] - 1s 80ms/step - loss: 0.0030
Epoch 86/100
16/16 [==============================] - 1s 83ms/step - loss: 0.0035
Epoch 87/100
16/16 [==============================] - 1s 84ms/step - loss: 0.0034
Epoch 88/100
16/16 [==============================] - 1s 81ms/step - loss: 0.0030
Epoch 89/100
16/16 [==============================] - 1s 89ms/step - loss: 0.0030
Epoch 90/100
16/16 [==============================] - 1s 89ms/step - loss: 0.0034
Epoch 91/100
16/16 [==============================] - 1s 79ms/step - loss: 0.0035
Epoch 92/100
16/16 [==============================] - 1s 79ms/step - loss: 0.0029
Epoch 93/100
16/16 [==============================] - 1s 82ms/step - loss: 0.0032
Epoch 94/100
16/16 [==============================] - 1s 82ms/step - loss: 0.0031
Epoch 95/100
16/16 [==============================] - 1s 82ms/step - loss: 0.0026
Epoch 96/100
16/16 [==============================] - 1s 88ms/step - loss: 0.0031
Epoch 97/100
16/16 [==============================] - 1s 90ms/step - loss: 0.0028
Epoch 98/100
16/16 [==============================] - 1s 89ms/step - loss: 0.0028
Epoch 99/100
16/16 [==============================] - 1s 87ms/step - loss: 0.0033
Epoch 100/100
16/16 [==============================] - 1s 84ms/step - loss: 0.0031

<keras.callbacks.History at 0x23187a54880>

 dataset_test=test
len(dataset_test)

137

real_stock_price = dataset_test.iloc[:, 1:2].values
real_stock_price.shape

(137, 1)

dataset_total = pd.concat((df['Open'], dataset_test['Open']), axis = 0)
inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values
inputs = inputs.reshape(-1,1)
inputs = sc.transform(inputs)
X_test = []
for i in range(60, len(real_stock_price)+60):
    X_test.append(inputs[i-60:i, 0])
X_test = np.array(X_test)
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
predicted_stock_price = regressor.predict(X_test)
predicted_stock_price = sc.inverse_transform(predicted_stock_price)

5/5 [==============================] - 1s 23ms/step

predicted_stock_price.shape

(137, 1)

future = model.make_future_dataframe(periods=48,freq = 'H')
future.tail()

	ds
590	2022-03-11 20:00:00
591	2022-03-11 21:00:00
592	2022-03-11 22:00:00
593	2022-03-11 23:00:00
594	2022-03-12 00:00:00

plt.plot(real_stock_price, color = 'black', label = ' Stock Price')
plt.plot(predicted_stock_price, color = 'green', label = 'Predicted Stock Price')
plt.title(' Stock Price Prediction')
plt.xlabel('Date')
plt.ylabel(' Stock Price')
plt.legend()
plt.show()

report performance

mse = mean_squared_error(real_stock_price, predicted_stock_price)
print('MSE: '+str(mse))
mae = mean_absolute_error(real_stock_price, predicted_stock_price)
print('MAE: '+str(mae))
rmse = math.sqrt(mean_squared_error(real_stock_price, predicted_stock_price))
print('RMSE: '+str(rmse))
mape = np.mean(np.abs(predicted_stock_price - real_stock_price)/np.abs(real_stock_price))
print('MAPE: '+str(mape))

MSE: 424.22564146136494
MAE: 16.173544601106293
RMSE: 20.596738612250363
MAPE: 0.031609390451010216

another model of LSTM

y = df['Close'].fillna(method='ffill')
y = y.values.reshape(-1, 1)

len(y)

684

# scale the data
scaler = MinMaxScaler(feature_range=(0, 1))
scaler = scaler.fit(y)
y = scaler.transform(y)

# generate the input and output sequences
n_lookback =50  # length of input sequences (lookback period)
n_forecast =50 # length of output sequences (forecast period)

X = []
Y = []

for i in range(n_lookback, len(y) - n_forecast + 1):
    X.append(y[i - n_lookback: i])
    Y.append(y[i: i + n_forecast])
    
X = np.array(X)
Y = np.array(Y)

model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(n_lookback, 1)))
model.add(LSTM(units=50))
model.add(Dense(n_forecast))

model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(X, Y, epochs=100, batch_size=32, verbose=0)

<keras.callbacks.History at 0x23193b30100>

# generate the forecasts
X_ = y[- n_lookback:]  # last available input sequence
X_ = X_.reshape(1, n_lookback, 1)
# fit the model
Y_ = model.predict(X_).reshape(-1, 1)
Y_ = scaler.inverse_transform(Y_)

1/1 [==============================] - 1s 660ms/step

# organize the results in a data frame
df_past = df[['Close']].reset_index()
df_past.rename(columns={'index': 'Date', 'Close': 'Actual'}, inplace=True)
df_past['Date'] = pd.to_datetime(df_past['Date'])
df_past['Forecast'] = np.nan
df_past['Forecast'].iloc[-1] = df_past['Actual'].iloc[-1]

df_future = pd.DataFrame(columns=['Date', 'Actual', 'Forecast'])
df_future['Date'] = pd.date_range(start=df_past['Date'].iloc[-1] + pd.Timedelta(days=1), periods=n_forecast)
df_future['Forecast'] = Y_.flatten()
df_future['Actual'] = np.nan

results = df_past.append(df_future).set_index('Date')

#plot the results
results.plot(title='prediction')

<AxesSubplot:title={'center':'prediction'}, xlabel='Date'>

results.reset_index(inplace=True)

df_past

	Date	Actual	Forecast
0	1970-01-01 00:00:00.000000000	334.45	NaN
1	1970-01-01 00:00:00.000000001	339.30	NaN
2	1970-01-01 00:00:00.000000002	333.70	NaN
3	1970-01-01 00:00:00.000000003	319.00	NaN
4	1970-01-01 00:00:00.000000004	318.40	NaN
...	...	...	...
679	1970-01-01 00:00:00.000000679	567.30	NaN
680	1970-01-01 00:00:00.000000680	550.60	NaN
681	1970-01-01 00:00:00.000000681	543.30	NaN
682	1970-01-01 00:00:00.000000682	536.50	NaN
683	1970-01-01 00:00:00.000000683	524.85	524.85

684 rows × 3 columns

test_forecast = results.loc[results['Date']> '2022-9-23']

pd.concat([df_past.set_index('Date'),results.set_index('Date')],axis=1).plot(figsize=(10,5))

<AxesSubplot:xlabel='Date'>

pd.concat([df_past,results],axis=1).plot(figsize=(15,5))

<AxesSubplot:>

auto_arima

import seaborn as sns
sns.heatmap(df.isnull(), cbar=False)

<AxesSubplot:>

import os
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
from pylab import rcParams
rcParams['figure.figsize'] = 10, 6
from statsmodels.tsa.stattools import adfuller
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.arima_model import ARIMA
from pmdarima.arima import auto_arima
from sklearn.metrics import mean_squared_error, mean_absolute_error
import math
import numpy as np
from nsepy import get_history
from datetime import date

def test_stationarity(timeseries):
    #Determing rolling statistics
    rolmean = timeseries.rolling(12).mean()
    rolstd = timeseries.rolling(12).std()
    #Plot rolling statistics:
    plt.plot(timeseries, color='blue',label='Original')
    plt.plot(rolmean, color='red', label='Rolling Mean')
    plt.plot(rolstd, color='black', label = 'Rolling Std')
    plt.legend(loc='best')
    plt.title('Rolling Mean and Standard Deviation')
    plt.show(block=False)
    
    print("Results of dickey fuller test")
    adft = adfuller(timeseries,autolag='AIC')
    # output for dft will give us without defining what the values are.
    #hence we manually write what values does it explains using a for loop
    output = pd.Series(adft[0:4],index=['Test Statistics','p-value','No. of lags used','Number of observations used'])
    for key,values in adft[4].items():
        output['critical value (%s)'%key] =  values
    print(output)
    
test_stationarity(df.Close)

Results of dickey fuller test
Test Statistics                 -0.717473
p-value                          0.842172
No. of lags used                 5.000000
Number of observations used    678.000000
critical value (1%)             -3.440032
critical value (5%)             -2.865812
critical value (10%)            -2.569045
dtype: float64

import matplotlib.pyplot as plt 
import statsmodels.api as sm
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf

df.head()

	Date	Open	High	Low	Close	Volume
0	2020-01-01	334.70	335.95	332.15	334.45	17379320
1	2020-01-02	334.50	339.85	333.35	339.30	20324236
2	2020-01-03	337.95	337.95	332.00	333.70	21853208
3	2020-01-06	331.70	331.70	317.70	319.00	35645325
4	2020-01-07	324.45	327.00	315.40	318.40	50966826

fig, (ax1, ax2) = plt.subplots(1, 2,figsize=(16,6), dpi= 80)
plot_acf(df.Close.tolist(), ax=ax1, lags=50)
plot_pacf(df.Close.tolist(), ax=ax2, lags=20)

result = seasonal_decompose(df.Close, model='multiplicative', freq = 30)
fig = plt.figure()  
fig = result.plot()  
fig.set_size_inches(16, 9)

<Figure size 1000x600 with 0 Axes>

from pylab import rcParams
rcParams['figure.figsize'] = 10, 6
df_log = np.log(df.Close)
moving_avg = df_log.rolling(12).mean()
std_dev = df_log.rolling(12).std()
plt.legend(loc='best')
plt.title('Moving Average')
plt.plot(std_dev, color ="black", label = "Standard Deviation")
plt.plot(moving_avg, color="red", label = "Mean")
plt.legend()
plt.show()

No handles with labels found to put in legend.

#split data into train and training set
train_data, test_data = df_log[3:int(len(df_log)*0.9)], df_log[int(len(df_log)*0.9):]
plt.figure(figsize=(10,6))
plt.grid(True)
plt.xlabel('Dates')
plt.ylabel('Closing Prices')
plt.plot(df_log, 'green', label='Train data')
plt.plot(test_data, 'blue', label='Test data')
plt.legend()

<matplotlib.legend.Legend at 0x2319a929940>

model_autoARIMA = auto_arima(train_data, start_p=0, start_q=0,
                      test='adf',       # use adftest to find             optimal 'd'
                      max_p=3, max_q=3, # maximum p and q
                      m=1,              # frequency of series
                      d=None,           # let model determine 'd'
                      seasonal=False,   # No Seasonality
                      start_P=0, 
                      D=0, 
                      trace=True,
                      error_action='ignore',  
                      suppress_warnings=True, 
                      stepwise=True)
print(model_autoARIMA.summary())

Performing stepwise search to minimize aic
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=-2771.058, Time=0.12 sec
 ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=-2769.636, Time=0.19 sec
 ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=-2769.592, Time=0.18 sec
 ARIMA(0,1,0)(0,0,0)[0]             : AIC=-2772.806, Time=0.04 sec
 ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=-2770.162, Time=0.68 sec

Best model:  ARIMA(0,1,0)(0,0,0)[0]          
Total fit time: 1.222 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  612
Model:               SARIMAX(0, 1, 0)   Log Likelihood                1387.403
Date:                Thu, 29 Sep 2022   AIC                          -2772.806
Time:                        00:15:23   BIC                          -2768.391
Sample:                             0   HQIC                         -2771.089
                                - 612                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
sigma2         0.0006    1.9e-05     32.866      0.000       0.001       0.001
===================================================================================
Ljung-Box (L1) (Q):                   0.58   Jarque-Bera (JB):               680.19
Prob(Q):                              0.45   Prob(JB):                         0.00
Heteroskedasticity (H):               0.33   Skew:                            -0.41
Prob(H) (two-sided):                  0.00   Kurtosis:                         8.10
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).

model_autoARIMA.plot_diagnostics(figsize=(15,8))
plt.show()

model = ARIMA(train_data, order=(0, 1, 0))  
fitted = model.fit(disp=-1)  
print(fitted.summary())

                             ARIMA Model Results                              
==============================================================================
Dep. Variable:                D.Close   No. Observations:                  611
Model:                 ARIMA(0, 1, 0)   Log Likelihood                1387.529
Method:                           css   S.D. of innovations              0.025
Date:                Thu, 29 Sep 2022   AIC                          -2771.058
Time:                        00:15:23   BIC                          -2762.228
Sample:                             1   HQIC                         -2767.623
                                                                              
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
const          0.0005      0.001      0.501      0.616      -0.001       0.002
==============================================================================

# Forecast
fc, se, conf = fitted.forecast(len(test_data), alpha=0.05)  # 95% confidence
fc_series = pd.Series(fc, index=test_data.index)
lower_series = pd.Series(conf[:, 0], index=test_data.index)
upper_series = pd.Series(conf[:, 1], index=test_data.index)
plt.figure(figsize=(12,5), dpi=100)
plt.plot(train_data, label='training')
plt.plot(test_data, color = 'blue', label='Actual Stock Price')
plt.plot(fc_series, color = 'orange',label='Predicted Stock Price')
plt.fill_between(lower_series.index, lower_series, upper_series, 
                 color='k', alpha=.10)
plt.title(' Stock Price Prediction')
plt.xlabel('Time')
plt.ylabel('Actual Stock Price')
plt.legend(loc='upper left', fontsize=8)
plt.show()

# report performance
mse = mean_squared_error(test_data, fc)
print('MSE: '+str(mse))
mae = mean_absolute_error(test_data, fc)
print('MAE: '+str(mae))
rmse = math.sqrt(mean_squared_error(test_data, fc))
print('RMSE: '+str(rmse))
mape = np.mean(np.abs(fc - test_data)/np.abs(test_data))
print('MAPE: '+str(mape))

MSE: 0.02654534780686956
MAE: 0.15256451524289494
RMSE: 0.16292743110621233
MAPE: 0.02433567466665604

we will implement Nbeats model

!pip install darts

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from darts import TimeSeries
from darts.models import NBEATSModel

import warnings
warnings.filterwarnings("ignore")
from nsepy import get_history
from datetime import date
import pandas as pd
import numpy as np
import seaborn as sns
sns=sns.set()
import matplotlib.pyplot as plt
%matplotlib inline
import pandas_datareader as pdr

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
C:\Users\WOLVER~1\AppData\Local\Temp/ipykernel_10092/1435460239.py in <module>
----> 1 from darts import TimeSeries
      2 from darts.models import NBEATSModel
      3 
      4 import warnings
      5 warnings.filterwarnings("ignore")

ModuleNotFoundError: No module named 'darts'

data = df[['Close']]

df = data.reset_index()
df.head()

df['Date'] = pd.to_datetime(df['Date'])

df = df.set_index('Date')

df

df = df.resample('W').mean()

df = df.reset_index()

# Selecting on required col
df = df[['Date','Close']]
df.head()

df.isnull().sum()

from darts import TimeSeries

stock_data=TimeSeries.from_dataframe(df,time_col='Date',value_cols='Close',fill_missing_dates=True)

df['Date'] = pd.to_datetime(df['Date'])

from darts.models import NBEATSModel
nbeats_model =  NBEATSModel(input_chunk_length=30, output_chunk_length=10)

nbeats_model.fit(stock_data, 
                epochs=100, 
                verbose=True)

!pip uninstall matplotlib
!pip install matplotlib==3.1.3

pred = nbeats_model.predict(n=30,series=stock_data)

pred.plot()

stock_data.plot()
pred.plot()